Description: Obsolescence of physical assets and processes is a major component of operational risk for some companies. A simulation experiment shows that managers, who consider the retirement and replacement of individual assets in isolation, have rational incentives, due to risk aversion and uncertainty, to defer the replacement of assets past a time that is optimal from the perspective of the shareholder who owns the entire portfolio of such assets. This results in measurable wealth destruction, —and a demonstrable opportunity to significantly enhance a company’s value. A practical decision model for the replacement timing of fixed, physical assets, which creates wealth by minimizing the long-term consumption of scarce resources, is provided.

Description: Quantitative operational risk assessment is essentially based on stochastic scenario modeling of operational loss sequences. Given the lack of reliable historical data in most cases, mathematical methods should meet even stronger requirements in terms of results received and include specific features, namely: rare event analysis, uncertainty analysis and human factor analysis, etc. In this paper, the analytical-statistical simulation approach ASSA is considered as the most flexible approach to stochastic scenario loss sequence modeling and compared with Markov chain, fault/event tree and Monte Carlo, in terms of scenario loss sequence model adequacy and calculation cost. Probabilistic risk analysis software PRAISE and some applications are given. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: Risk management and capital budgeting are two critical components of the corporate decision process. They often need to be considered jointly at the corporate level because of their natural interaction through the dependent risk exposures and other synergetic relationships within an intricate corporate structure in a dynamic business environment. . This paper develops an integrated framework that aligns these two important corporate strategies across business divisions to optimize the streamlined enterprise strategic goal in a multi-period setting. The proposed integrated framework can serve as a practical guideline for corporate executives to optimally coordinate capital budgeting and risk management at the enterprise level.

Description: This paper demonstrates the potential synergies from applying actuarial techniques to operational risk analysis. It also demonstrates practical techniques, grounded in actuarial science, to solve operational risk management problems such as risk cost allocation and risk mitigation cost-benefit analysis. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).

Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).

Description: The costly, time-consuming and complicated process of enterprise risk management (ERM) can be improved in many companies and made less tedious for managers by using reasonable data and templates obtained from peer group entities. The models used to calculate economic capital (EC) often underestimate its value because they do not consider decision-maker perception about risk. We assume here that managers, as the decision-makers, have appropriate business understanding and they can provide substantial information about risk characteristics regarding all business processes. Therefore we are focused, in this paper, on collecting data from managers across the different businesses to derive the appropriate knowledge about risky events, the importance of particular types of risk, the relationship between the risk outcomes and the level of risk control in a particular industry. We conclude that the collected data has a high potential for use as a benc hmarking reference and analysis for improving ERM models for individual businesses.

Description: Increasing uncertainties and volatility in financial as well as real goods markets increasingly reveal that controlling systems based on deterministic and pseudo-exact values are insufficiently eligible to manage the business activities. This raises the question how management systems can be modified to generate the adequate management impulses in volatile periods. Risk management deals from identifying, evaluating and aggregating chances and risks, which again determine the potential fluctuation range of relevant performance indicators. Everyday corporate life shows that risk management is considered important; but the corresponding processes to identify, evaluate and report chances and risks are seldom part of established decision criteria. This “paradox” lets assume that risks are managed in various differing and unsystematic ways. The combination of traditional controlling methods and risk management is not primarily a methodological challenge. The behavior of the whole organization, practiced over a long-year period, has to be transferred into a new corporate culture of transparent dealing with chances and risks. This paper describes the necessary change management process, which has to come along with necessary methodological modifications.

Description: Given the lower incidence rate, use of decision tree techniques like Classification and Regression Tree CART in understanding credit or operational risk becomes quite challenging. A commonly adopted solution is biased sampling approach, where more weights are attached to bad customers to artificially hike the incidence or bad rate. While adopting this type of biased sampling approach, question of the best weight arises. This paper adopts an iterative approach in identifying the best weight. The best weight for bankruptcy BKO profiling problem in hand occurred when the incidence rate was around 50 percent where entropy reaches its maximum.

Description: In this study the author will review some correlations between the operating income per employee and some elements of company structure for the S&P 500 that are proxies for management control and supervision. An analysis yielded the following general observations: 1 companies in which the chairman and CEO roles are combined generate more income per employee than those in which the roles are separate 2 companies with a lead director and the chairman and CEO combined typically do better still 3 the more executive officers a company has, the higher operating income is generated per employee although this decreases marginally and 4 companies tend to gravitate toward a structure with 10 executive officers and 10 directors this is a fixed point of an iterative decision function. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.